NCA Computer Vision & NLP Workloads 5

Question 1

What is the purpose of 'streaming' mode in NVIDIA Triton Inference Server when handling NLP text generation?

Accepted Answer

To return generated tokens incrementally as they are produced rather than waiting for full completion

Answer

Streaming mode lets Triton send partial generation results token-by-token to the client, reducing perceived latency in interactive NLP applications.

Question 2

Which data format is the standard interchange format for exporting trained PyTorch or TensorFlow CV models before TensorRT optimization?

Accepted Answer

ONNX (.onnx)

Answer

ONNX (Open Neural Network Exchange) provides a framework-agnostic model representation that TensorRT can parse and optimize into a high-performance engine.

Question 3

In a multi-node NLP training job using NVIDIA NeMo and Slurm, which communication backend handles gradient synchronization across nodes?

Accepted Answer

NCCL (NVIDIA Collective Communications Library)

Answer

NCCL provides highly optimized collective operations (AllReduce, AllGather, etc.) over NVLink and InfiniBand for multi-GPU and multi-node gradient synchronization.

Question 4

For a real-time NLP sentiment analysis service, what TensorRT optimization would you apply first to reduce FP32 model latency without retraining?

Accepted Answer

FP16 (half-precision) conversion

Answer

FP16 conversion halves the memory bandwidth requirement and leverages Tensor Core acceleration with minimal accuracy degradation and no calibration data required.

Question 5

Which NVIDIA NGC resource provides pretrained text embedding models that can be integrated directly into a semantic search or RAG application?

Accepted Answer

NeMo Retriever embedding models

Answer

NeMo Retriever provides GPU-optimized text embedding models on NGC designed for semantic search, vector database population, and RAG pipeline integration.

(NCA) NVIDIA-Certified Associate Practice Test

(NCA) NVIDIA-Certified Associate Practice Test

NCA Computer Vision & NLP Workloads 5